1. [1] R. M. Abreu, M. A. de Sousa, and M. R. Santos. A cost-effective local positioning system architecture based on TDoA. In International Conference on Telecommunications, Springer, Berlin, Heidelbergpp, pp 858-865, 2004. [DOI:10.1007/978-3-540-27824-5_113]

2. [2] Y. C. Cheng, Y. Chawathe, A. LaMarca, and J. Krumm. Accuracy characterization for metropolitan-scale Wi-Fi localization. In Proceedings of the 3rd international conference on Mobile systems, applications, and services, pp. 233-245, 2005. [DOI:10.1145/1067170.1067195]

3. [3] H. Mehmood, and N.K. Tripathi. Cascading artificial neural networks optimized by genetic algorithms and integrated with global navigation satellite system to offer accurate ubiquitous positioning in urban environment. Computers, Environment and Urban Systems, 37, 35-44, 2013. [DOI:10.1016/j.compenvurbsys.2012.04.004]

4. [4] D. Madigan, E. Einahrawy, R. P. Martin, W. H. Ju, P. Krishnan, and A.S. Krishnakumar. Bayesian indoor positioning systems. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, Vol. 2, pp. 1217-1227, 2005.

5. [5] M. N. Husen, and S. Lee. Indoor human localization with orientation using WiFi fingerprinting. In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, pp. 109, 2014. [DOI:10.1145/2557977.2557980]

6. [6] A. Savvides, C. C. Han, and M. B. Strivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. In Proceedings of the 7th annual international conference on Mobile computing and networking, pp. 166-179, 2001. [DOI:10.1145/381677.381693]

7. [7] C. Y. Cheng. Indoor localization algorithm using clustering on signal and coordination pattern. Annals of Operations Research, 216(1), 83-99, 2014. [DOI:10.1007/s10479-012-1219-x]

8. [8] I. Guvenc, and C. C. Chong. A survey on TOA based wireless localization and NLOS mitigation techniques. IEEE Communications Surveys & Tutorials, 11(3), 107-124, 2009. [DOI:10.1109/SURV.2009.090308]

9. [9] J. S. Leu, M. C. Yu, and H. J. Tzeng. Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals. Computer Networks, 91, 329-340, 2015. [DOI:10.1016/j.comnet.2015.08.032]

10. [10] R. C. Eberhart, and Y. Shi. Tracking and optimizing dynamic systems with particle swarms. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546), Vol. 1, pp. 94-100, 2001.

11. [11] A. Chehri, P. Fortier, and P.M. Tardif. UWB-based sensor networks for localization in mining environments. Ad Hoc Networks, 7(5), 987-1000, 2009. [DOI:10.1016/j.adhoc.2008.08.007]

12. [12] B. H. Cheng, R. E. Hudson, F. Lorenzelli, L. Vandenberghe, and K. Yao. Distributed gauss-newton method for node loclaization in wireless sensor networks. In IEEE 6th Workshop on Signal Processing Advances in Wireless Communications, pp. 915-919, 2005.

13. [13] K. Yu, and I. Oppermann. UWB positioning for wireless embedded networks. In Radio and Wireless Conference, pp. 459-462, 2004.

14. [14] F. Álvarez-Franco, H. González-Velasco, C. García-Orellana, M. Macías-Macías, and R. Gallardo-Caballero. Using GAs to obtain an optimal set of codes for an ultrasonic local positioning system. In International Conference on Computer Aided Systems Theory, Springer, Berlin, Heidelberg pp. 845-852, 2007. [DOI:10.1007/978-3-540-75867-9_106]

15. [15] H. Mehmood, and N.K. Tripathi. Optimizing artificial neural network-based indoor positioning system using genetic algorithm. International Journal of Digital Earth, 6(2), 158-184, 2013. [DOI:10.1080/17538947.2011.606337]

16. [16] H. Zhu, B. Huang, Y. Tanabe, and T. Baba. Local Positioning with Artificial Neural Network and Time of Arrival Technique. In 2008 3rd International Conference on Innovative Computing Information and Control, pp. 509-509, 2008. [DOI:10.1109/ICICIC.2008.340]

17. [17] H. Mehmood, and N. K. Tripathi. Cascading artificial neural networks optimized by genetic algorithms and integrated with global navigation satellite system to offer accurate ubiquitous positioning in urban environment. Computers, Environment and Urban Systems, 37, 35-44, 2013. [DOI:10.1016/j.compenvurbsys.2012.04.004]

18. [18] P. Claro, and N. B. Carvalho. Local positioning system based on artificial neural networks. In International Conference on Artificial Neural Networks, Springer, Berlin, Heidelberg, pp. 699-708, 2007. [DOI:10.1007/978-3-540-74695-9_72]

19. [19] S. Ngah, H. Zhu, K. T. Chen, Y. Tanabe, and T. Baba. Artificial neural network based model for local position systems, 2009.

20. [20] K. G. Margaritis, M. Adamopoulos, K. Goulianas, and D. J. Evans. Artificial neural networks and iterative linear algebra methods. PARALLEL ALGORITHM AND APPLICATIONS, 3(1-2), 31-44, 1994. [DOI:10.1080/10637199408962524]

21. [21] A. Cichocki, and R. Unbehauen. Neural networks for solving systems of linear equations and related problems. IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications, 39(2), 124-138, 1992. [DOI:10.1109/81.167018]

22. [22] K. Mathia, and R. Saeks. Solving nonlinear equations using recurrent neural networks. In World congress on neural networks, pp. 17-21, 1995.

23. [23] D. Mishra, and P. K. Kalra. Modified Hopfield Neural Network Approach for Solving Nonlinear Algebraic Equations. Engineering Letters, 14(1), 2007.

24. [24] G. Li, and Z. Zeng. A neural-network algorithm for solving nonlinear equation systems. In 2008 International Conference on Computational Intelligence and Security, Vol. 1, pp. 20-23, 2008. [DOI:10.1109/CIS.2008.65]

25. [25] A. Margaris, and K. Goulianas. Finding all roots of 2× 2 nonlinear algebraic systems using back-propagation neural networks. Neural Computing and Applications, 21(5), 891-904, 2012. [DOI:10.1007/s00521-010-0488-z]

26. [26] Z. Nemec, and P. Bezousek. The Time Difference of Arrival Estimation of Wi-Fi Signals. Radioengineering, 17(4), 2008.

27. [27] H. Liu, H. Darabi, P. Banerjee, and J. Liu. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080, 2007. [DOI:10.1109/TSMCC.2007.905750]

28. [28] R. Zekavat, and R. M. Buehrer. Handbook of position location: Theory, practice and advances. John Wiley & Sons, Vol. 27, 2011. [DOI:10.1002/9781118104750]

29. [29] M. Kaveh, M. S. Mesgari. Hospital site selection using hybrid PSO algorithm-Case study: District 2 of Tehran. Sepehr, pp. 7-22, 2019.

30. [30] M. Kaveh, M. S. Mesgari, and R. S. Paland. Multiple criteria decision-making for hospital location-allocation based on improved genetic algorithm. Applied Geomatics, pp. 1-16, 2020. [DOI:10.1007/s12518-020-00297-5]

31. [31] M. Kaveh, and M. S. Mesgari. Improved biogeography-based optimization using migration process adjustment: An approach for location-allocation of ambulances. Computers & Industrial Engineering, 135, 800-813, 2019. [DOI:10.1016/j.cie.2019.06.058]

32. [32] S.M. Mosavi, M Kaveh, M Khisheh, M Aghababaei. Design and implementation a sonar data set classifier using multi-layer perceptron neural network trained by elephant herding optimization. Darya Fonoon, PP. 1-12,2018.

33. [33] M. R. Mosavi, M. Kaveh, M. Khishe, and M. Aghababaee. Design and Implementation a Sonar Data Set Classifier by using MLP NN Trained by Improved Biogeography-based Optimization. In Proceedings of the Second National Conference on Marine Technology, pp. 1-6, 2016.

34. [34] M. R. Mosavi, M. Kaveh, and M. Khishe. Sonar Data Set Classification using MLP Neural Network Trained by Non-linear Migration Rates BBO. In The Fourth Iranian Conference on Engineering Electromagnetic (ICEEM 2016), pp. 1-5, 2016.

35. [35] M. Khishe, M. R. Mosavi, and M. Kaveh. Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network. Applied Acoustics, 118, 15-29, 2017. [DOI:10.1016/j.apacoust.2016.11.012]

36. [36] M. Kaveh, M. Khishe, and M. R. Mosavi. Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integrated Circuits and Signal Processing, 100(2), 405-428, 2019. [DOI:10.1007/s10470-018-1366-3]

37. [37] A. Lotfy, M. Kaveh, M. R. Mosavi, and A. R. Rahmati. An enhanced fuzzy controller based on improved genetic algorithm for speed control of DC motors. Analog Integrated Circuits and Signal Processing, 1-15, 2020. [DOI:10.1007/s10470-020-01599-9]

38. [38] A. Lotfy, M. Kaveh, M. R. Mosavi, and A. R. Rahmati. An Enhanced FPGA-based Implementation of Fuzzy Controller using a Personalized Microcontroller. 34th Power System Conference, 2019. [DOI:10.1109/PSC49016.2019.9081499]